Paper
18 November 2014 32-channel hyperspectral waveform LiDAR instrument to monitor vegetation: design and initial performance trials
Gang Sun, Zheng Niu, Shuai Gao, Wenjing Huang, Li Wang, Wang Li, Mingbo Feng
Author Affiliations +
Abstract
A hyperspectral full waveform LiDAR instrument prototype with 32 wavelengths and a supercontinuum laser as a light source were designed for monitoring the fine structure and the biochemical parameters of vegetation. The optical design and instrumentation are described in this paper. Components of the instrument included an X/Y scanning platform, a supercontinuum laser source, a receiving optical system, and a 32-channel full waveform measurement module. The pulsed LiDAR instrument is able to measure the 32-channel returned full waveform laser signal. The distance of the interacted target can be obtained from position information in the recorded waveform. And the spectral reflectance can also be obtained from the intensity information in the waveform. The performance of the measuring distance and spectrum was evaluated. The initial performance trials indicated that the instrument is capable of high measurement accuracy and has the ability to detect the biochemical characteristics of vegetation. The experiment also indicated that the instrument has the potential to generate a 3D point cloud with spectral information. Therefore, the instrument can play a significant role in detecting the vertical distribution of biophysical and biochemical characteristics of vegetation.
© (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Gang Sun, Zheng Niu, Shuai Gao, Wenjing Huang, Li Wang, Wang Li, and Mingbo Feng "32-channel hyperspectral waveform LiDAR instrument to monitor vegetation: design and initial performance trials", Proc. SPIE 9263, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications V, 926331 (18 November 2014); https://doi.org/10.1117/12.2066788
Lens.org Logo
CITATIONS
Cited by 3 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
LIDAR

Vegetation

Sensors

Remote sensing

Reflectivity

Telescopes

Prototyping

Back to Top